{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas - DataFrame\n",
    "* DataFrame是什么\n",
    "* 创建DataFrame\n",
    "* DataFrame选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## DataFrame 是什么?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "最常用的python数据结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "和sql的table一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "和R语言中的data.frame一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8rQMHDtS3rzVmwoQJ9RXucXFxNXk3H4lw/+uvv6yPwjdnzpw5Nd35p59+erB7KpXK2NhY6G6fZaZNm1ZT/33u3LlH+1ssy3799dcPVkvt7OwOHDhQX+F+5cqVS5cuWYwvfyTCvbKy8oFzzvr5+QmzcDW58IWM8g/A4sWLoWKDcH9gT5nFvqCgoGDhwoUP4BgS6NevX1FRkfltb968+WDCXalUxsXFmdwtLi5u4sSJ9QqUNfZDz5w589kU7pcvX7aoPg3CneO4a9euvfHGGw+2cQ1FUXUaDYua5J9//uF5/o033rD+txwcHE6ePGk8MWJxn/iaaNu2rZXa/datW8ZxktZXg+nTpz+Ya7+BCvf9+/fXPlbbvXu39dPxtRAVFWVvb1/TpLOHh0eLFi0CAwObNm3q5ORU00Dwl19+sV64SySSSZMm1SRZHl6479y5s6bnFIlEPj4+gYGBgYGBjRs3tre3txhqhjH+77//zO+cmppqMQBAqVQ2bdpUuK2Xl5fF+NopU6ZAX/uMY76UwvgTr1q16hGasI8//vhhwgJdXV1rUboWhft///3Xrl27mvqPhxTuGo1myJAhD/NGQUFBubm5Fm9er77QmBYtWtQeQgCAcDdM6VhcshUaGmoeyvXwOwAOHTrU3Jg8QuGuUqk6duz4kA/5559/PlPCvUOHDsXFxS1btrT4tAbhXlxcXN9sHOZs3ry5vsJ9+/bttfhba8LDwyMtLY3neYZhBg4cWN/LhwwZwjBM7YV55swZd3f3By6KqVOngnCvx7xGTRXUeC5j1qxZ165dq2nqrU5KSkrat29v8eYvv/zygQMHCgsLhTMZhsnMzNy4cWPr1q0tyojExEQrhTvG2DxpK0mSCoXCwcEhNDS0rKzsgYV7YmKiRUeISCQaP368kCRHOFOn0yUlJf3xxx++vr7m57dq1crkMXieX758ufmZo0aNSkxMpGlaOKeoqCg6OvrXX3/t2bOnYXDi4OBQU/kAzw4ZGRm1LF3AGHft2nXt2rUmA8sHYOnSpTX9Smho6PTp01esWLF69eply5Z98cUXrVq1snhmt27daooONxfuGONaYuEcHBwyMjIeRrhbVFFCaH7Pnj1nzZq1atWq1atXL1my5OOPP64poN+i6rp69ap5KIJUKn3jjTeWLFmyevXq1atXz5kz580333RxcTExLH/88QfUahDuVlLTvksREREmZ9Y0c1UvVq9e/Vg97nPnzn3IJ3R0dKwlhu3JC/c2bdqMHz++pqc1/vQPIKBN8PX1rcWZbVGTvPrqqw/m4xeGHA+8Xfe6detq97XXpNoxxra2th4eHp6enm5ubrVUaWtWM4Jwr2bTpk1WTmd06NDh888/P3XqlDUL14yZPXu2+Q3lcvnSpUtrukStVn/yySfWe5QthsoYI5PJ3nzzzQ0bNiQlJQmJ8KxpJDUJd4uT9R4eHjt37qzpjYqKioYNG2aNv8E80VhgYGBN8x4sy167dk14+I8//hg62ucCa7KmeHh4vPXWW//++29ubu4D+OBv3rxpcf7az89vx44dhhGgAb1ev3btWouh8OZ9fy0ed4vBrO++++633367aNEik5VS9RLuBw4cqGkm12LAaFVV1Y8//mgenkeSpPHEscC8efPM57K3bNliftvy8vLdu3ePHj1aWHfbvHlzcLeDcLeeK1euWMxjuGbNGvOT33rrLZPTnJ2dR4wY8cUXX8yfP3/u3Lk//PDDpEmT/Pz8amp9wcHBJiGvt27dcnR0lMlkCoXCZFWiYRisMEMqlbq7u8fHx5s8YX5+fuPGjc07rPHjx3/33Xfz5s2bP3/+d999N3LkyFpyVX3yySfPjnAXiUTmHwhjLIx2/ve//xkuT0lJMc8SExIS8vbbb8+cOVN492+++WbIkCG1ZK6sZbWbNevcMMYymUypVCoUitq3l3Jycjpy5EizZs3MXZlCTag95D00NFSj0Vh8zsrKyu7du1u8qmvXrhs3bkxNTdVqtXq9vrKy8ubNm4sWLbLoVfHz80tPTwfhbi3Tp0+3fuAlRLZ8+eWX586dq3P2hOf5nJwci/7FtWvX1nmteUSgp6enxZnu2oX74MGDb968WftvWS/cr1y5Ym7s5HL5iRMn6pzfMF/61r59e5NRhPk5Q4YMqbOsrl+/np2dDR3tc0FeXp71q7SVSuXw4cMXL16clZVl/U9YzO/evn17YcK0JmJjY81FQFhYmEWTXadwf/XVV8+ePVtL3gDrhbtOpwsPD7c4h1u7bj58+LC5dh81apTJacOHDzd/69pLOCEhYcyYMStXroT6DMLdehiGMUnnJzBt2jSLylJYgKhQKEaMGLFnzx7D7LQxFRUVmzZtsphmkSAIk2EqwzDFxcVFRUWVlZWxsbHmYZlNmzbNzs4uKSkpup/i4mKLPb5hZs/Hx+ezzz67dOmSSqUyPy0jI+Prr7+2qA6DgoJqSn5Sk3AXLBJXH6wU7uYjpXHjxm3bti0uLk4oNOM7zJkzx+CemD59elRUlMURRXJy8kcffWTx/p06daoplqF24S6Xy0eMGLFv376srKzS0tKioqLr16/Pnz/f4ty+QRkb5hUdHR2nT58eGRkpfNn8/PyTJ0++++67NXnERSLR2bNnLT6nRS++RCKZN28ey7I1+THHjh1rftU333wDwt1aWJa12IPWqeDDw8M3bdpU0zhMYNGiRebXjhkzxpoHS09PNzcrFr3atQj3adOmWeOwtF64W8wK8v3331vzRhcvXjSZkbexsTHJKGe+vKP2CTXgeSQmJqbOnIPmc8pC511n0Nq1a9fM/Vuurq51Dl95nt+7d6/5yhCLLu1ahLuLi4uwiKp2rBfuu3fvNj+zdevW+fn5df7K77//bj4WSk5ONj7HPH1Ehw4drLGc1jgvABDuxlhcTdG/f3+LJ3/66aejRo2yJuVAZGSkxbwu3333XU2XZGZmmi/BDAwMtDJ3qkBpaWmfPn1mzZplcS2sCQsXLrRoMWrKYWVRuJMk6e/vH2A13t7eoaGhJgts6hTuFEX973//M7/KmKysrG7dui1cuNA85NUci4sWJBJJQkJCfYW7t7f3kSNHLF6VkZFh0cdhEgBZU3jS/v37a1pqbHElcUFBgcVVvEuWLKm9NPR6/csvv2xyVUBAQHFxMQj3erB169aAgIAHCH4KDQ2Nioqq6bbm8SEURV28eNHKp/rggw9MLjeeq6pTuI8ePdrKH7JSuKtUqjZt2phLIpPg3ZrgOK5///4ml//666/G5/Tr189iIZ8+fdo8wgF4fklKSqpphrEWCIJo3bq1xSgOAxb3Ua8li5EJ5irWoiOkJuGuUCjMY1EeUrhbnECwZmzA87xGozGP4P/7779rfxeMcZ19DwDC/QH44YcfLFp4iyfTNG19pNxXX31lfuexY8fWdH5GRoZ5aHKLFi3qFYjCcZz1HZNWq7W4eP3ff/+1Xrg/AE5OTiZj9dqFu1QqtSaxcr3evaSkxKKzpiYJXpNwt7GxOX/+fO09Sy3rRFu3bp2Tk1PL5Vu2bKl9Ya4xS5YsMT9zxIgR1hRIQkKCeUbLgwcPPo6GTKAXlFGjRkVHR69evbpjx471Wv0QFRXVvXv3v//+2/xPeXl5Z86cMQ97CgsLs/Lm5lnYU1JSeJ635lp3d/cff/zx0ZbSrVu3rl+/bh6KY2XSLoyxudkSNls10LlzZ4uF3Ldv386dOy9cuPDGjRsMwyDgOadx48ZHjhxZsmRJvVzvHMfFxsaOGTNm/PjxhYWFFs85d+6c+byq9VlTzFXspUuXrH/CmTNn9urV6xEWVGlp6dWrV00ONmrUqPYtDI37YPPRsskbmfsseJ7/5JNPBg8evHfv3srKSqiuwKPCYvptvV5v0apTFGXN/peGyDHzhadqtfqxvg7G2Poc8xKJxOJar8fdxAiCqNc+oAsWLLAm7Wy93l3YgMn8eFVVVb3e5bPPPqs962Ljxo1rSoSNMf7ll19qz7M+evTovn37WvmNzAUeSZIWFyia07x5c/PKcPjw4cdRAagX2KDY29tPnDjx7bffvn79+n///bd79+7U1FSdTlfnhSqV6r333rOxsTHZijU1NbW8vNzkZB8fn4KCAmFMX/ttTfYDMwwGqqqqrEkcO2DAgDq3iqwvt2/fNj/o4+OTl5dnjX1UKpXmEbeZmZnG//vKK68sWLBAq9WanMYwzJUrV65cufLdd9+FhIT06tVr1KhRrVq1qn1JCvAsI5VKP/7444kTJx46dGj79u379+9XqVRWXrtx48akpKRdu3aZLJDKzMy8du2aycn9+vWzfnhgns48Nze3oqLCmn0YAgMDzWfJHpKEhISkpCRzp4712aPNo4rT0tKM/7dPnz7mETWC++fgwYONGzfu0qXLiBEjwsPD69yrFQBqx6JAJ0nSeglYE0LWDr1eb6LVnqnXt7gC/pl6yAEDBtQUkv7U393V1bWmLfxMVITFiZ1BgwZZnNI3eZ7+/fsfP37c5HhFRQXHccbjn/z8/LNnz5qcFh4ebtH5aJHOnTuvX7/e+MidO3dAuD8IJEmGhoaGhobOmTPn2rVrx44dO3To0OXLl2maruUqmqY/+eSTVq1aGff6BQUFLMuanLlp0yYrU9kIdQhjbCzxk5OTS0pKrOmzX3rppUdeOPn5+eYHf/jhB4uNpKY3MjkSHx+v1WoNG2K3a9fuiy++qCXNllarjYyMjIyM/Omnn9q3bz9mzJi33nrLwcEBesTnFBsbmxEjRowYMSI9Pf3IkSPHjx8/duxYZWWledsxISIiYtKkSf/9959xl5+Xl6fRaMzr2Oeffy4sFarDwFFUYWGhSCQybu/5+fmlpaXWCPeBAwfWkj7iwcjKyjI/eO7cualTp9ZulAREIpG57s/MzFSr1YapxX79+nXq1CkiIsLiHZKTk5OTkzds2ODi4tK/f/8hQ4b079//wTadAYDS0lKLtbSWS7RabVJSUk5OzsmTJ6uqqiw6j0mSLC4uNm/7T4ySkpKUlJQ7d+5cvHixJjEqEonMp6yfNd555536blpXWFiYlpYWHx8fGRlZ07UikaheU5cW6dWrlzXT+76+vl5eXtnZ2eb22ZpfCQ0NNdFdCCHz/ig5Obm4uNjcd6PT6YTVxrX/ilwud3JyMncSqVSqR96JUA3HvohEorCwsLCwsGnTpmVmZh46dGjHjh2nTp2q6fy8vLzff//9r7/+Mh6imZ9mZaBLTSdrNBorp5bqu/jPGsrKyh75G5WXlxsLd4TQrFmzCgsLV6xYUeetBB/8woUL586d+/bbb0On+Fzj5+f3v//973//+19hYeGpU6eOHj16+vTp5OTkWi7Zs2fPihUrPvzwQ8OR4uJiE5cbQighISEhIeGBH6y4uLigoKCWrHMGzGPbHh6LEUGXLl16mF4wOTm5srLSINwlEsny5ctffvlli4ME4ycR/A4BAQGTJ0+eNGkSyHegvkRHR1vfW2VlZa1bt07IalLfvuaJERERsX79+l27dgnrU5/BJ2RZtk4/iICrq2u9dpU6derU33//vXfvXmF96uN+ESufTSqV2tramgh3qVTaoUMHay4XskOalJj5YKywsJDjOJODGzZsEFYsWCmHTEYIKSkppaWlINwfjYJv1KjRhx9+OHny5LNnz86cOdM8sElg7969c+bMMcwmP6Z6bE30jtAZPxfFy3GcidIiSfKvv/5q0aLF3LlzzUe05mRnZ7/zzjvR0dG///77szYxCjwALi4uo0aNGjVqVGVl5ZkzZ37//ffTp0/XtLBhzZo177zzjkGDCoulHvkjWRkp+zgiSaxs7/VtdCa+yZCQkD179owfP15QSLWTmpr65ZdfbtiwYd26daGhoVBjASspLy+Pj483P25xB7SVK1fOnDkzLy/vWX6dr776av369Y+jkdaEjY3N6NGjKYqy0tCxLGtvb1/T3u0mODs7WwxoMaegoODzzz/funXrk1xyZu6irkkQm0tqGxsbixv3WrzcmtMszh3p9Xpzz5H1VFRU1DfoH4R7HWCMe/TocezYsY8++mjlypXmJ+Tm5sbHxxs67wfboa1OrJzGehzy5XG8EcbY4tTnp59++tprr23evHnz5s1CIr/a7/PHH39IJJJffvkFescXBqVS+fLLL7/88st79uz58MMPzac+EULXrl2LiYl5HK5uE6X7gpWt+Ru1a9fu1KlTixYt2rx5c+2ud4EbN24MGjRo7969D7/xO9BAuHLlSkpKivlxk51xEEJz58797rvvnuV3UalUI0eOPHbs2CPpBK0/2c7ObtmyZY/JMScSiaxZbFBcXPzKK69cvnz5CZd5fWN4TAr5YS5/Ypgv8APh/mhq9u+//x4XF3fhwgWLw1Dj0aF5pJSbm1uLFi0eTAcImX0sZqt9MlgcsPr7+/v7+1s5E2cCwzBOTk41TQx5e3t/9dVXn3zySVRU1OHDhw8cOCDs/FrT3X777bdXXnnlAZIMAs84Q4YM8fDwGDBggEUnR3JyskG4K5VKiqIeuRPIym7ycYyWrXSV1bcPszgId3FxWbBgwbRp0/bv379x48br168XFRXVcp+CgoIxY8acO3fO4vY3AGDCP//8Y37Q2dm5ffv2xkf279//qFT74xtyf/31149EtSNL8dNPCyst2JQpUx6Van+SPpFHa58ffjm1ReqV/6chCveTJ09ev379s88+q298hVQqHTx4cJ3C3cPDQyQSmcybuLm5HT582Dik+znCYvccHBy8d+/ex/ejMpmsa9euXbt2/f777+Pj4/fu3btt27Zbt26ZN3iGYbZu3QrC/VlGpVJVVFTUnpDLImFhYe+++67FGZWSkhLDvx0cHMRisYlwb9Gixc8//0xR1IN1EizLtmzZ8mmVmMWtl3v27DljxowHm5PleZ6iqFpmjR0dHcePHz9+/PjMzMxz587t37//6NGjJSUlFru9lJSUVatWzZw5E+o2UDuRkZEWk2SHhYUZJ0BjGMZidXJxcRk9enT79u1dXFzMG7JIJEpNTf3ss89MYsCsWcD9AERFRS1fvtxib/j66683a9ZMoVCYP6RUKt21a5dhs1UDDxNc8eQ5ceLE5s2bzX0BoaGhr732WpMmTWQymbmtEHLDb9iw4bl+d2Msxu04Ojp6eXk9sGcW1ZAvFYT7PZYtW7Zjx45bt24tXry4vqusLPam6H7PXEBAgL+/v0kKxdjY2CNHjgwZMuR5LLGgoCBbW1uTRbfC+Md8Y6ZHjlgsDgkJCQkJmT59+n///fftt9+ar1w8evSoTqd7XuL7GyCbN29esGDBtm3bTHxs1lDT0iLjNdM+Pj4KhcIkJD0jI6NZs2bNmzd/HkssICDAfOIuPT09LCzscZh4Y3x8fMaMGTNmzJi8vDwh6b7FlYVHjx6dMWMGJGYFah+xf/XVVxbDACZMmGD8v1evXjVfaBEWFrZr167aJ3aSk5NFIpGJcH9Mq54OHjxo7ib//PPPf/nll9o9punp6eYHn6+lWQcOHDA/OHv27DonSSxaj+cXNzc389ndRo0aXbhw4TGFST8wL84GTNeuXTt48CBCaM2aNZ06dTp06FC9Lq8pSYWxH0sul/fu3dv8nAULFli/K0T1jrXPBo0aNTLfPUqlUi1YsKBew8qHfCOKokaPHr1z507zqKHi4mKLqW+AZwGWZTdu3Jiamtq7d+8VK1bUN6ClpuA/40bn6OhovpGwWq22mKf8uaBJkybmg5zU1NQ1a9Y8sWdwd3d/6623Ll++PGfOHIu2tKbNsABAYOrUqRaTOrRp02bAgAHGR65cuWK+1vPnn3+uMxzrSQZdmL9LYGDgnDlz6oxzeHaiYh4Yi8nLZ8yYUeeFL9hKocaNG3t7e5scjIqKqil5CQj3R8Aff/xhGJrHxcUNHjx41KhRUVFR1lybkpKyY8cO8+Nisdhkkc3IkSPN10NcunRpypQp1lTi4uLi8ePHP8keuk4s7r6+devWWjKvG5OUlDR8+PBatgeLi4v74IMPrMkk0Lp1627dupmPCl4Ay/iisn///vPnzyOEKisr33///YEDB0ZGRlp/+cmTJy0eN+nRLSbrXbdunTBQt4a4uDiTfcGeIlKptE+fPubHf/nll5iYGCtvEh0dbRxQZD6wsbi3mgkkSc6YMaNnz57mWsRi6lsAQAiVl5e/++67NaX3nTlzpskaJ/PdQsRicdOmTev8odTU1MeRkcOiADVvTa6urtZk8TPZKfy5Q6VSmS808vLysmbdpzVG5jnCzs7OfJNsnucXLFjwrImQF0S4x8TEbN261aS4t2/f3qVLl6FDh+7atauWTi4iImLo0KEWJ7w6depkYl969+5t4k4QWL169bBhw2rZJUuv1+/YsaNnz56bNm2aPHmyeUjZ0+L111+3mLrru+++mzRpksUdmgSqqqpWrFjRu3fv3bt3jxs3rqZR6dq1a//6669u3bpt2rSp9uXVOp3OeEWBgEwms2ajHODJw7LskiVLjI8cP368Z8+eY8eOvXjxYp2Xb9iwweLOZfb29iZhWkOGDDFPC63Vat98801rFpNt3769c+fOo0ePfna8yOPGjTOv1Xl5eSNHjoyNja39WpqmlyxZEhYWNnny5JrCSc+fP9+uXbuffvqpzoQGJEk2btzYtFcgCJlMBjUcMBcxp06d6tatW02+p7fffnvo0KHmV5kfsWZnpaVLl9bLp0sQhLmDnGGYOqOuLU4aWxNMn5qaah7k/dx9U4t9sTWia9u2bS9YDR8xYoT5wZMnT06fPt2ay6uqqqZMmfLff/89ic/2AjBy5Mja27O7u3ufPn0WLVq0ffv2I0eOHD58eO/evb///nuvXr1qCeVcvny5+W/VEvBka2v78ccf7927NyMjo6KiorKyMjMzc9++fQsXLmzdurVx3JtIJNq4cWNNr2MeG4AQunbtmvUFMnnyZPM71PSLtez86ubm9t133x05ciQ/P1/ISHrnzp3du3fPnDkzICDA+ExHR8dTp06Z3Dk5Odmw4ANj3LJly19++SUuLo5hGJMzGYaxuE7xtdde44FnEouTVAaPWqdOnRYtWnT27NmKigrjzy1slPvee+/V1O5effVV899avHhxTbrzgw8+SE1NNb9Eo9Hs37/f2FvfvXv3/Pz8ml7n1VdfNb//6dOnrS+Qb775xvwOv//+u8WTjTeZMkYul8+cObOwsND8kvLy8s2bN4eHhxtOHjNmjFarNT/z5ZdfFk5o167dhg0bhL1ULJKTk9OoUSOTZ2jUqJGwUyDwgjF16lTzKjd+/Pjar9LpdOnp6Vu2bOnVq1ctoSNt2rSx2L5+/vln85N//vnnWn6RYZgvv/zS4q+8/PLLNV1VUlJi7sinKOrcuXMmZ9I0zXGc8ZEuXbqYXKhQKKKiomp5yMzMTPMpYsOQw+IlR48eNT/Zw8PDYiuuL/Hx8YbtLwy0bt269qs4jjNfqe/q6nr79u1arkpMTGzbtq3Fd9+yZYv1mmTTpk3WvFppaan5iiYnJ6e0tDRrLr906ZL5BELv3r2FvbeNYVnWfAZS4J133snNza2lGE+cOCEkQ1MoFAcOHHisDfkFEe4fffTRIx/S9O7dW6fTWfy5b7/9tvZr3d3dmzdv3rx5c/OQKWPNUdPXfcLCnef5MWPG1PI6GGMfHx9hOWAtW9I4OjreuHHD+LafffaZ+WlKpTIkJKRfv35Lly5du3bt+vXrv/zyy+bNm1ucm9uwYQN0t88m1mw3LcyJd+zYsWfPnj179uzWrVtQUJB572I8oD169Kj5b6nVamPBaoKLi0unTp1mzpy5fv369evXL1y4sFu3bi1atDBfItalSxeLKv/JC/f09HRfX9+a3sjLy6tnz54///zzunXr/v7775kzZ4aHh5srbITQyJEjhfwwBk6cOGFyjo+Pz3vvvbdjx46rV69WVlbq9XqapgsLC3fv3m0xZdPEiROhejcc4e7m5tanT58eNRMSElLnTjdeXl4xMTEWf/Ts2bPmo3SZTLZu3TqLkv3ChQvmEQv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JZy9PNBhShFAzP8WQXu40y5+4VHjjTsXRiwUWvbEIoakLb169+1lFFDGir0eAtzz2dvmRCwWCM2X3qdxf1ifNnNyC5ViJmLx8o/SvbWkGl03vMJe2gXYEgZMyqg6dz69Sswih8ir62z9v7f0jXNgKE4T70zRhlw9kGcZkoQO8xs5sI1WIEM/rtdy/82Iu7cmo9hHeLudq3m85dIDXyGnBts5ShPiuI/z+/iY6J6l6PHpue1r7gV4ypcjihZ2G+g79rKXSUcLzfOehvutmRBVmVDeqs1tT2/b1FMvIWuxXVmLZxZ3phiPDPm3ZY0yAxIZi9NzVQ1n/zI0RDFDU4axOQ30DWjtiAmXEl5UVaITzQ3p5TPi5PSUhMEIcy9+JKl756WVNFY0QykmuTLxc2H6Al+55NmHAU3DmSch/9mcevnDPpE5/t9k37zWTO4gRj4ryNO//ELPjeI7wp5/X3RnQxa1Nc1uLbiGMUNd2Ts38FJdulLTwV/r5K27frthzKtfgVdqyoP3gAV6IR4jnb90q6znhfGGpHiGk03N30qu6h7sis26D45GjnXjLgtD+3d2RhGBVzEdzY//anir8NadQezu9ytfLBiE+LVO96cA9x3zP9s6bfwz19Fcgnq8o0n04P3aTmdseISSVkqcvF24+kHW3z8BrZrUdN9QPSQik41b8m/rhjzGCmlm5M+2tIT6BjZSYIi7ElBh87f8b4bd8brvq1VEMd+Bk7vDPLguTzhdiii9eL+7TyVWrhVYJPKgOEBHpcaUJd9WSTCma8FP7wM6uwurSO1cLl310Seg4NFVMSY7Gu4W9xelohYP4zdntWnZzo0RYW8UcXXvn0MrqwLa8lMprR7N7jG7EsqaKUNgDdfwP7Zp1dKFEWFNBH1yRePzu3FpmfHnsqbxOQ3xpHVuLa+DU5hR1RXV78W/lMO6Hth6NlQjhklz1plnXEyIKEEKaSvrsv6lv/uBAkFhbxSTHFBtef8LP7QM7u/IczyOkqaC3Lbhx+W5bjj6aEzrAmyAxzz2vY2ORlPx7X8bZqGLDkdkfBn7xdhOZnQhxKC9bPXHWtYPnqr0V81bdfincpXmAgra0po7AqEd75wBv+YXrxWEtHdx95bHXSw6erb5WYUNtX9ihTy8PhBDiUXR0ce93Lwh2TKVhUrJUHdo4Ih0yt8DuzpJ/f2rfo5MrEhN0Jf3u99c37KsWWpl56uQslZuLlCD4hNTKrUfuDR4GdnFbP7etq7cccXxpgfa92dcN/YgxEil58Ezef8eqPfpSCblxbrvXBnsjEYG07G8bkj/75Ybwp+XbUt982TvAS45IfC66WHO3yk0Z02jhtyEII4Qxorn/DmaNnnZVWDZ7+mrxlZulnds4aXVPzgKDcDeF1rJ9xzdp0tZJU6lPu1nW/91mJEloKmhSRMjtRK26uRmEu7qKoXUsSVoIN3LzVwyf2kqqEKnK9Qghr+Z2Qz8NWv5xpLCioDhblRpbGtLL3XzVi3dzuyFTgsRSUvB8+AU7DPkkaPUXVwy2L+NWWYtOLpaHHAgRJL51vkCrqraMQV3deowJ4Bi+qlSPCdzxFd/bV4oidmcghFiGv3U+v0lbJx4hw/kIobJCbX5alZu/Qlhi3yLcZcB7zXKTKzwb29q5yfyDHWgdJLIA6ulu17Jrdt0bTI4e4D3/0yCO4bXlNELI2Uny1/cht5Ir41MrEUKlFfSmA5ltglsjS8J9wnC/v75rQylFpfkalYphNIybk2T7wg5JGarE9Cq5lBzcy4OuoAkCk2IiKNixfbDDobudSkkZXZNXZMrYRv1f8tSX0ZyakcrISSP91u9N1+o4QVUUFOsQgUkJPnm5MP/u1JlMQv46LdjTy0ZbpkcI2SpFv37ZKiKmxODjv9csEdp9Ms8QOjn2ZZ9xI/z1aobTMBSJJ73Z6OD5vL2n8xBCZRX0sYjCoOZ2CKHyynsRMqk56syMKh8vOeJ5RKDBfT2nv9ussFjXPEDRyFse3MyO1kOrBB4chuZcfOQTf+lQmqPOSal09JA16+CsrqBJElMSolEbR88mtmk3qmNgVBX6mu7T/93mrXu6qSsYLc+TFNFvYtO0G6XxEdUj9tjTeV1e87cYdDL4gxYtOrtoKhgtz1MiYuCk5qmxpcnXqoVm7Kncjq/61BTpTZC4JE9z6+4sGUHgwZNbuPkrhbliOxfpkI8Dk6KKhJFGUnRxaY7a0dNGp2YMc9csy+fcqfQNcpDISJ7nbWzF/d9tRutYz8ZKuZ3YJ8geIcQ/tzNaJIHVVfTa3Rn3rOgwv+8+bMHqWMECu7vLVs1q03X8udRsNUIov1j7z+Hs2VOCEG3B6f7RG41++7o1tqGKc9VaLctW0b4esp2LO6Rkq+NTKl0dJX26uukraIrEhJho28axTaDdmStFdy1wjTXny7eb9ujhri+jWRUjs6HeH+W/+WCm4M6gGb6wVI9ITFD46MWC0rvDM1sF9etXwa6uMsECOzhIfvsqODK2JOvu7NA9dzuHdp3INXzAicP8XnvVR1/JcDxDkfjTd5rsP5d34lIhQii/WHfyctF7rysRQmVGFjg5S52bpfLwsEEcjwj02iDvaQnlVSqmia+8qa+imZ9C/2RXMINwNxv8cbybv8KziS0msUhCMHoOY6SuossyVNm3yy/vu+dR41iepTmKsiDcAzu72TpLtFXV9V6nYvxbO3g1szWE8WXGl7Xu6W6+6qhlNze5vdhwoVbFNG7r5OavzE+rntHLTKhRuCOEWJpLjb0XYtg01MnWSaq+W//EMrJ5R5eIuw04+XoJQ3MEiR3cZEL2AIRQ+s3S3yaed2+kbN7RJSDYwcnLpt/EppSY1GsYRs8xNAdxMkD9nD0kvplcYRwk885QH0RgPV3tn9BqWGc32agBXrOXJwhHzlwt0lTSQrT3ff48G+qTsY0pEaEt1SltSFsbSk9zMgnRr4tbv24YCdnB9BxGRF6uJjVbfSKyMPru3ChCSKPnUA2Vt0tbR6RhhWgTWs/5uMnkMkqrq+5maIZHBEZ67oZREGd4iENwM6Wh79dpWRc3Wd9wFxPhTmCsUjOXjQJ/+3V2RUqR2PByClGvTq6CcEcInYsumjK+CcLI39PGcMmRiwXtXj8d0syuX2fXjq0cGvnIv58WTBAIaVlEc7SOYzlolcCDw3O8WEa16uaGhdyIGLMMR4mIklxNSY4qPqKwwCjwvaZRoo2tqGU3N62KFTQuy3AyBdW6l4dBuBekV5Xna+xcZSYX2jpLAzu7aqsY4UKG5mRKUXBPd4Nwz0utrCjSKh0tZ6egxERuUkVFUfWIWukkaRLqRJBYmNMmSOzRxNajsW1mfBlCqKxAm5+mcvKWi6WknbM0+3aF8Po7F908829KQIhD0w7Ono1sXfwVH/3VWa9laB3H0hxTs+l4Diwwha8nVl41MkHvDPFBPDI41LVqxtNbPqKvp2EF0ekrRbSaIcykjYOt6MMxjTFG2lKdnYKyU1B6mreRUQN7uCN8zwITYiInV5OSpToaUWgc+F6TBaZIHN7aAalZjucxQqye8/WQySSkIbicYXiEMa9nbybdu1vXdk4tAhRGFpjx9rbp0cFl84H7pj1JjMsr6atGHcGArq5ITol5Iwsc7nLi7nTTheji90YFIIQCjCzw7lO5F2NK2rSw69/ZtUNLh0Y+NvO+C8EIIQ2LaM483TAI96fjfiBITKvZtNiK5GslqbElhZmqokyVxZwqvKU1/+4BCuMKyvM8KSKcfeQG4V6ar7EYZuPmr+A54wuRSEI4edsYhHtpnpbnELIYLIMxQ3PGS4si92WmxpQask8SJC4zGoyWF2orSnS2ThK/Vg6+gfbpcdUNW11Bp1wvSbleghCydZZ4NrVt2cWt7Uueds5SHvQBUE8wRSSkVqnvth03J0ljbzln0vfTXLsW95bPp+eoM/LUzf2VJl4uPw9ZoL9Cp2WqTTniMUYcj2g9J5KReVmqqLiyE5FFMYnlSZlVGbkas/ZRgxEksY+7jXHdJghMmi1HYxguK//ePRt7y0mM2bvtnEcI8XyrJrbmP6rSMHeMFswtWp+0/XC2cfp5Q4ApQig9V1NeobdTiHp1cG7qq7iTUX1hUan+RGThichCjJG3m6xNc7shvTyG9/N0sBeDagceiXZnaE4kIQvSqzLiym5fLcpLrizMVJUXak2rdA13cPK0UdiJjPs1huYdvWQEiQV3j1bFVJXpHTxsTC509raRKSnjBsjSnLP3vdM0lbS6nLZzllr8XWGZluF/1RX61VOvGDdAjkPF2feaWH5aRVAXF7GMat3L49bdkGiO4wszVYWZqsv7s8RS0sVHHhDiGNrfq1EbR/S8Ny+KuJVcaTA4vu4yf08b1swChwbZG/4vOUuVXaj197QxsS1NfOTNfGyEqDwhcyLGiON4Ws+JpGRORtWVm2UnI4tu3Cm/k6Eytpa1W2CJmPB2kxkWP/CIJ80tMEZ6mss2EjBNfUxUFkIIBzdVmv4ogStUdLKRjZ27InHNjnRDkgCKxLcz7tnn1By1qoqW21AvdXLxcZdl5lW/RUGJ7ujFgqMXCwiMfNxt2gbaDevtMaSvp52t6MlbYBDuFhDLyKSo4sMrE5OiSx5sgkxiQ5nocpLANkZB7YzOwloyjJFEdp/9QjxPUoRMQRldWGMcFcZIp2aNAwHzUioNi28sjE/0HKNjeR5J5eTQT4PWz4gyt9EVRbqKosKEiMITG5MHTWrWYbAPQ3MIdAJQD+WOdEadhExKKuSmrQNxyNVJYljfo9Kwag2LMDKpae7OUpGIMAklpEhMs/zCvxKXbU3Jytc+SHsXEWKKuNceeUQQ2FZOFZTcF4zJ88g4H46tnMImHRGH5GbrTzDGFVWMcbx+1K2yqFtlNT2MWsPq9RzL8m6u0sXTWo2bEWWYGjY8RmaeJjNPs+9M3k9rb//8eauhfTwgwB14SEiKoHXs4ZWJl/ZkVpbqHuAOMoXIpEXwPC9TiEiK4FgWIcQxPGNpix+ZQoQR5o1aO88jmVJkUPwswzM0V9OIgeeQxiiqgdZxxrkZzNGoWISwXsu2H+iVdrPUEPtqQK9ls+9UZN+puLAjre1Lnq9+EmTnLH2Os7ljpDWywDYySm5DmeoPHrk53ZvQqFIxGi1rXuDuLlJEEgjdVxQiitDquXkr41f8l5pX9CA1RywixCLinrnnEUlgpZwySajFcUijM7bAJML3dxE8L5dR5q9fXskYh7JE3iit5WFUGpamOYbmfLxsfv2y1YTvr1Wq7gsZ4niUnqtOz1XvPpkbtPbOwi9aDezm9oQtMAh3C6o94VLhuq+uau//WhIbyrelvUhMxkcUCNqaEhFiKYV43nwgyTKcqQlD6D5Fjs1lCeJ5xLKmOVt5hIx98PXK6CoSk4QIW9TZLMORFMEyPEaI1nEBIY6Tl3Y6vz312vEcIam8CWX5mi1zYkQSsl0/Lz2oBKB+Hcd9FdriWJjj7huCWsy/prChTC4mCIwwnrIgdtWOtPtUCIm9XGWdQxyuJ5YnpFZ7U5Q2FLJ0WxspSZHYmtcQie5NHmv0nOmbYGTNRnoyCUmR2OJ5epojSSy4grQadnAP92Mruyzfmvrf8RzjkHcDdzJUr3955cCfnfp2cnmSS6OAFwyCwCzDbZlzPebkfVn2CBI7etj4trRPiy0tuZseRCKnLLpOGYbneR6bNHfOqMFjy90Xy1jwj/EmBgFba2IIAotqTkGj17I8y/OI5zmeIPBr04IDWjtc2puZahTLZ9wjRx/NUZXpJ/zcgRQRz++Es9l4ysKHMH45grBc4AobykS2CDsoTZpzzbD43mCBfd1twlvbX7lZlnQ3elBpI7KYgdxGShIErtNHijESkfdbYGSqoxiLm0ljCz9n8Sd0eo7AmGF5hJFWw77Wz8vbTbZ8a+quk7km8l3gVkrlyKmXj/zVpUsbB+0TXGgEwv3+70tgvYY9sua2QbXLlKKOL/s07eDs2dTWwV0Wdz7fsAgGY0SQiGUstIHKEh1Bmtov45GASEpaUO4IVZXqSOMLMeYYzjj/Y/WFFh0PPBJLSUp8r2YPer955+F+WhVTk7GTyinBkcCxvGcT5Zuz2w54r/mdq0V3ooqyE8szE8pN9mq9sCM9qKsbRREczM4DVsLxDrYiAlfnK6zSMOVVjKujBBkvliBxVr7GYLjtlCI7OYXM6pi9kjLpT8Ri4uiFAmPV3jHYYdzLPmHBDk185A52opFTrxiEO0VZFg48smoOiSKws53Y8L95RTrTByRwqtGUvbETSGS0EmbF9yFDenkY5wbG+K6O53kCY1slRTOckNYptI3j6jCXH9IqT14qPHW16FpC+fWEMuNBjp7mVvyX2rODE4ERNErgAXWAhIg6km2s2pu0c2rX38s3yN7JSy6SECumRBqEO0lZ3v6lokjLsTxJ3WtNGGOdmjGEQBAUpsQWWmB5oYbneYzv9YgYI62KNqynIimCFBE1tVKMkcTmnpJx8rb5aHnnGtPz8YiSEPRd3xMlIrq/3qjzUL+0G6UJlwsz4kozbpWbzDwnXi5KjCwM6e3xvHqsON7R9t5sf4WKqVAx9krRfRaYwJlGsYUOtiKlDWU+ULFXmKbCE0mI3cdzjVV717ZOYwd5hwU7NPaxUdpQr3wcaRDuIgo/+G5iPBJR2MneyAIX6hB/f6UgcEqWuQVGChlFkdgwF7BhXmjfcBeVpgYLTGC5jGIYHmPEs3x4e+fwTq7zUipPRBaeuVoUHV8ee7vcuGqpNOzKHamdQhyeZEZIEO73F4eIyL5dnnnr3k4QI75s1XWEv07DsDRHiQl1OW3NfbISylmaM3xIjDGt4wqM4qhcvG2EPYTNL+QEp3v1hUinZguNlru5+Mhr9D3wvEhCOHnaGAL+1JW00lEikhAGA0eKSIwRwghjzLG8MCQgCFyco6Z1bFGW2sXHpsNg7/YDvarK9JXF+pTY4vPb0zPvJn/Nvl2hLqftXKSgEQBrew2Wb+KrsJFVrzQqKtUnplY2b2J7X9IYEb4Ycy9Vma+7zNtNRrO8iSMcm1d9ijhkNC3euqntwb86O7pKeTXDcTzHo0Lj6aOHqbM8wmIisPG9AMpLsSW5xVoPp+q9AEUUoa2iT14uNPdjKeWkn6espLz6ScqrGFsXqfzuLDDGmBAR1bs1EYijeWHjNoxRUkaVTs/fyahq3cxu7Gt+Y4b5VZRocwt1p64W/bkl5dbdKLjLN0tVGtZGSiJI5Q48mMcK41vn72VrbdzW8f0lHWVKkV7LIh7Rek5VXnc7KivQFGWpvFvYc3cnf0gRLsxQGfS3jVJk6yxlGY4U3Sf9S3I1JTlqV38Fo7+n1AvT7/V6cnux0lFSU14EnkOO7vcWvGoqaJLESieJYXEXSWJMEYLxwATWqxmW5gkKa1VMeaFWq2JUZfqgLq6N2znq1Gxlia4gU3X9WE7EngzDhqmZCeXt+nuh51O48yzfzF8hFhGCpcop0CZlVPn7yu+zwBQ+f/2eBfb3lHu4SFnWdMNH8+WqiMAHzuXd85u0cjj4Vyelg4TXMDyPaJYvNMok81DmiedJCdkiQGE4cP56cVGZzkEpEsL3xSKiskx/2mw7LZ7n7RSUj4dNwl2DWalm7FylCoMFJjBBGVlgPafXskJtuZ1epaO5pAxVuyD7t0b5jxvhV1GsyynUnogs/GNLyp27ebojY0u1epYiCP5JhRGDcL/ffhFIXUEbh4n7t3TQa1mdmqVEmCBwxi2j6Kjq3VItfKqb5/Kzb5f7tnQQYu9kSirmZJ6wgL1amrR0QNhCTY45mdtrTCP3JkptFYMwkilFN8/mGbaCIwjsG2RfS90gKaJxO6fbd7MvxZ7K7TE6QOko0WtZgsQkRWyZc728UOvZRClTipqHuXg1t2Npbuv8mJhTeQSJNZV0cE+PSYvDdFpOJCGdfWwCQhwcXGXLProk3JBlecvTbABQAwzL+3vZhLd2MOypsXxb2oBublIJodVxGCOJnEpLqdp94p71H9rbUyQjdWoG1RnBQiDD1ksIoUY+ckdPG12xlueR1IbKylXHG63xwA+5TzTD9WzvrLChhBFIbqF2yabkn75qLRVUCIl/XZlkHj3J87zCRtQlxPFafLU7YMvBrAnD/GQSUqdnRRRmeTRqSiRCqLGP3M1J8lInl+CmthVVzLgZ0aeuFJIELqukJ73m/9ePoayWsZFRzQNELcKcbWTU299EGUqYB8kOPBwGhzpCyM1fqXSSVhRpEUIyhaggvarIOFdSDY2S0XOX9ma+EeLIsRxL85SEYHScsRffq5mdrYuU1rEmgSw6NRO5P/O1L1txLM8xvEhKaNVMzKl7F3o3t1M6SugadDNDs17N7ZSOksoSHUKoqkx/7XhOn/GNaT3Hc7xUQd2+XLRzUVzT9k4EiX1a2Dfr4Kx0lNy6kP/vvBidhqV1LMfwU1Z3adzOiWV5hYPY3k3Wurt7+s3SzIRyg/fh+e30GIZv4qPo0NL+wvXqcKDlW9N6h7tKxYRWz2GMJApRYmLFgTP3LPCwPh6EmGDUrEmyawtmBuP0nHuu+mb+CqWbTFek5XkkVYgyUypvG2XLeNj9Zxm+T0eX+atvC4um0rLVy/5N/X5KS5JhEcKIwItXJN4w272V43gHW1F4sINBuG/clzl2sA9JYD3NiUWEjuZHfRwhl5F+njaertJ+4S6BjW1LyvRvfHU1IraEwKi8ipk6vsnC70O4KkZuQwU2VgaFu2ICfzw/1mCBn/DCPwIMlolfzTjUBCF0aksKo2dtnSQcy5/anCLsbFqtYmler2UtBuPq1Mw/c2OzEsptbEVyW3FSdMmuxXGGv3o2Ufq1tLeYEF1Vrt8yNyY3uVJuK7JRihIuFe5ZEm/4q0+gnXfzGnM28whxHB/Sy8OwtVN+WtXORTeryvUKO7HUhorcnxm5NyP+YsGJDcn7lyYkXi6kRITEhnLyluvUjDDGuHk278TGJMTzMjklkZIF6VXXjHY08GqqtLEVQW4ZwHo4jhdLiHeG+hmOHL6Q//7s63mFOqmdSCKnbt+pfGtGlGGDPT8Pm7GDvTkr4wV5pDRaun36StGJY9kSO7HUTpSaUTX5hxjjBaaVVczDzBRpdVy7QLtB3dwMR35elzT+iysR0cXnrhS9NyPqG6Omel9Xh9HrA7zFd72METEl0369qdIwUgcxKSGXbknecTxnx/Gcn9fdmbrw5uWbZYgkbG3Fvh6yShUj5BLesC9zw9ZUkkAipQhLydyE8n1GsqZ9kL2NjALpDjwMUvm9dhR3Pj/hUoHCXmyjFGXdLt+24IZxlIhWxdRU2S7sTD+6LonnkdJJwui4fUsTDF4khFBIb/eapovP/JN6cnMKxkjpJNGp2T2/30ozGgOH9HGvZWjKMryTp01wT3fDkUMrE2NO5oqlpNJBUpav3b8sPvt2+ektKSc3Jh/8K4FlOB7xLn4KhJCmkmb0HMfxu3+/lZtcKbUhZXIRz/HXjuUYZ6rxbm5rOXj6eYDleBsF9dYQX8ORnSdyPpp7vbBUL7UTS2youFtl42dE5d3doaK5v2JUPy9Wx1nnoeNtjWrO4YsFF87kSezFUlvRnaSKyT/EGC8wrahiHmZWUKdnw4MdXurkajgya3nCe9OvRl4vPRNZ+Na0K7NXJFrogHiESPzGQG9DXPvJy4XfLInT6Tmpg5ig8OK/7+w/m7f1SPbP6+58+tON67crEIkdHcRebrJKFVNexSCEVu5I27YrgxIRIqUIS8ism6WHzt2b6e3Q0l4iJp+keAePu4kJ4Fx85G4Bivy7cbEXdqZlJpS5+MgL0qoM42+DOleV6R3cpRarYmZ82R+TLjZq44gxun2lyDjQvPe4Jja2Yp2asTgCTY0pWfLehUZtHDmWv3OlyHib0j7jm0hklE7D1Dgi1XOeTWw7DPI+u7V638foozlZiRVeTW21KiYputjwqB6NlZ2H+TF6lkeo8zC/mBO5wsauPMfv/u1W9NEcBzcZQigrsbzIKI9Sh8E+MqVIDzunAvVBr+Nee8lz2xH3PaeqnTrr9mSculLUoZW9VsdF3ig1yGuM0cKpLb09bbRqBlvTbTB8nzCXDXurE/eWVdIjp17p3t6JIomzUcWF9yfHyCnSIobHD+P1wXjGxGZHLxYY9ubYuD9z490dFjFGdkpRpYoxWQGi07Fd2jqO6Ov5z6HqSNCl/6aeiyoJaqwoLNWfMdrOsEd75zGDvGktS1H483GN957KzcjTIIQ0Ova92ddX70z3dJGyHH/5RmlG3j0v19uv+orFBCSWAR7CY8U36+By6+7exuWF2lWfX2nUxpHn+KSoYs39yT0qCrU1yWie4/f+Hhd9JNvVT56XUmk8ydyojWNIb0+mhv37WIbb8cvNKweynL1tcu5U5CbfmyhrEe7Ssqs7reNqabkcx3d9zT/6aLawBYq6gl4/Pdq/tYPcVpSZWFFoFKTae1xjB3eZVsW4+Mh7jmm0+7dbhm73z/cv+gTaiySEqkyfGlNicJD5trRv3tGFeZ73OKO17NjB3v8dzTl6N6f+8m1pRyMKQ4PsVRrmUmxp8d2AFhFF/PpFK1cXqVbDENZ4yDnUN9zFsF9pYYluyJTI7qFOGKHTV4pL7t+rK6dQix5iHxieRyRFfPNus1OXi4QIdZ5Hq3emr767VbxggSuqaJPqqdOyvTs6v9Ldfc/pan/HL+uTjl8qau4vzyvWnTOywAO7ur3W11OvZcUUnvZOkwNn84S+qVLFjP8mavnWVDdnCcPwl2JKsu8uhCBJ/PYQP5LCNPPkPigId9Oxu62ztMfoRtt+jDW49DLiyjLuZu9v2t65rEBTmKFCCFWW6ArSq5x9bMybtH+wQ15Kpapcf8No+kmg+6iAdv29LGpfTGD/Vg7Zd8qrSvWxp0wv7DO+SXBPd72WMTzYfV49QxPVs4Pfb1GYqYq/eG/biwKjNNIIITsX6RvfhtjYiYXJR1snyWvTWq2fHlVRrBOMb/rN0vSbppP+7Qd6h73sAzunAvWF43gxRayZ1Zamow/eDUlPy1Gn5dy3kIgi8S9TW73W3+u+oWkN9dxQ20f09VyzK92wm3dpJW0YHtgpRT3bOx04my/kaYmKK9PqWMK8/+ctLE+1qEy0Ojakpf2WBaHjvokuNtsFcHgfz9Ag+xlLbpnfiuP436a1SstRRcRUN6vYO+Wxd+5zBLQIUKz4LkQmJrR6jtXzAb7yP2eEvDnjakUVgxDS09y56GLzR/rszcbD+njqIKUM8BAwNNdhkFfk/szcuxvcVJXqYu/O6igdJT5BdoYg+PS4Mo61EDDp4C6zUYqy71RkxpcZlkUJ2LvJhk9tJZaStM5CkkEnLxuxlMxNrjTvd5x95EM/a0mJCFrHYhIjkx1M7/6b0XPezWxf/7r1plnXhdB2nYZJjDRdcNLzjUYdX/XVqVmEkF7Ldn3NPzepMvLuwLu8UFteaNrtyu3EQ6YEye3Ez3UuNZbjpWJy/dx2b06/evJy9RxIcqbKZLc4iZj4/evgQT3cdcYyg7dojO/WHB37xkCvtbvSr9zVSMVl+l0nqmuOk724c4jjgbP5QmaCyzdLGYarwQCb5dOzZIK1Oja8rdPGee3e+f6aSbJIhNDYQd5NfBWz7m7kZ3x/AuM/ZwRn5KsNIYvXEsquJdxXS0Oa2S2d0ZoisY7mtHq+RRPbP6a3fuf7aLWGRQjp9Jx5AD1C6OsJTft3dX3CfhMIlTHzDmqYTkN8X57cQnx/SilSRPQa2+i9RR1adXdHqDqiJuFSIcda8OF1HuY3ZmYbk53epHJq0Psthn7akucsx6TyHN9jdMDr01vLjZJXIIRkStGQKUEvT27BsXev45GQtUZY5WMc3sOxvFhGvjUvtM/4Jjb33wchhDBq1sF50m9hfsGOhpBBvZZt3Mbpgz87te7lbrJsSEBhLx74fvNRX7fGGEGcDPAgzYrhHOzFWxd1+HFKkK+HzPyEzm0cDy7t9Om4xnoda9w4hAyMUjGBEBKLzPKusryNjFz/Q7ue7Z1N/tQiQPHPT+2XzgjxdJESGIlIHJdcGXu7QriJEDwvFRMUiaViEiPT/IxSMUHg6t8liHt9lk7DDOzpcXJ1l0kj/Rt521AEFlE4LNjhxylB//0apjDKbiEREVIxKTQXmuFdnCR7l4RPfj1AKTd1l5AEHtrbY/+fnZo3Uhpyimk17Cu93Y/81bl/Z1eL2Sq9XKW/fxX80+etaIaDOBngIT1WcnvJ+B/a+gTamfzJu7ndOz+FDvk4SG4nJkiMCZSZUFaQXmWeW8bWWfrOgvbGISsCjdo4Tloc5hNoR9cwvHT0sJnwc4dAoxAIgWZhzv9bFObRSFl9IY8QQiIxiXF1x4eJe8mOdRo2dKD3/xaH+bVyMP8JpaN46JSgVz8JNHS+PMdjAo/8uvWwz1s6WLJIGONmYc6Tfu/YuI3TC5ABWU9z7s6Snb91nD25hZerha2serR3OrK886SRATqDBeaRwepWW2Cz3awZlrdTiDbND+0U4mjyp+CmttsXdlg8rZWro5gkEEXgmMTyW8mVYooQjJ5wW5JAUjGB8f15q/G9P6H7F8XqtMyw/l4nVnWZOMzPz0NGkVgswp1DHBd90Wrjzx3ERgJGIiYl4uoNOvQ05+0uO7C007vD/WzMdtugSPx6f6+9f3QM8JYb9tzQqZlRA70OLevUO8yFtDT54Och++u7kNmTA/VPfDYGP+OrmsoqSiPPR+VITxVIIkle8oQKhUCUiMxLqUyILMxNquA55OonD+zi6t3cjmV5bSWtqaqexycILLcXEySuKtUtnnCh7O5WYaOmt+4zvkn27YpbF/LTb5YRBPJoogzq4uYWoKB1rCFDLUnh0jzN4gnnDanTx81p2+U1/6yE8lsX8jPjywkSezWzDeri5uIrN74QY6TXstWPwSNSRNjY3peniSAxJSKKs9WpN0pSr5cKy3TcAhQBrR18g+wpMWluQykxwXMoL6UyLa4k81YFz/IcxyscxP6tHXwD7R08ZIye49gnWls4rHfWtfem+3To1NbB1gn61yfG9ZibqrzzoU6HMOIe4WJkisSUlCws0F6+WXouuji/WIcwauQl797OqX2wg9yG0mnuyz+KMSqvZNRaVkjQZCMj7RQWgrklYkKtY89HFR+LKCws08tlZLe2Tv27uDo6SvRaprBETwu5vXiklFNKG4pHvE7PlZTThlbsZC8iMTbaggkVl+mFjGAcjxyUIpm0ugMgCSyiMJKQSERUFmmLS/QIIWd7scJWhER48ao7n/9yQ7hJaJB95OYeDHtPVVMUQVH4TmrVxZiSizElDMtzPB/SzK5LiGPbIDuCwOYdgERCMgwXm1hx6UZpdEIZRohheR83WXiwQ/tW9m6uMr2WfbS5WQnMqRn7iKLh7l5N2rZpDW3hqXD1alRaYWy6ciePWPSksgFQEkJXxSZFF925WqSuYKRyqkmoY/NwV5mC0mtZValeqGk8j2QKSiylRBLi8Krbh1ZWBxZ7NbP7bG0XgiSSoorizheoK/Q2tqIW4a6N2zmJpcS9qVqMRBJy75JbJzcmCwcCWjt8srILy/B3ooriLxRoqmi5vTiwk2ujNo6UmLhvQptHqvLqx0A8ktmKRGLivnG+lKS1bEZcaXJMSWG6miCQxIYKCHHwD3Zw9LAR+sH7+3oskhBVxbr0W2WpsSXlBTqCxIhH3oG2Pi3svZrbUWJMa5+oLOMQK+OVTdVvOYuyg233I4QeuQXOz9dGxpacv1ZSWKZDCDX1UXQPdQptZS+TkjoNe79+RmWVtEZXnSJPLiNtLVpgCVGlZs5eLT4RWVhcTittqB6hTv26uNrZinVaprC02pbyPLJVUAoZySOk1XGlFdUWmCSwk73Y2AHK86i4TM/erW8OtiKp5K4FJrGIxEhKIgpXFOhKynQIYxd7sVwpQiI89/f475ZWrzXqHup0Zl03nf6eJ4gSERSJE5IrL14vibhRwvGI5/i2Lew7t3Fs08IOI6Q322ZLKiX1ejYmoeLSjdJrieUERgzL+3vIOgY7tm9l7+IsfQwWmK2knaPKR3UI6+rgYGf5O4KJtOT5RoyedfNXeDazFbKqcyzP0BytZXkeSWwo6d1spjzHW9SyPI/0GsbeTdL99QBy7N076Fkhu1ZtP80jWsM6etr0HNtY+GmW5Rmd6YU8j8Qy0vAYiOdNEq5zLE9zrL2brL23d/irvoaDLMMxNGfR88HoOYyRRxOldwtb8nXC8EMszbEMV+eTA0CdMCzPqBhHO/HgHh6D+3pWr/FgeURzNM1pzRZv8DxytBM5O1RPHLEcz1pqbjo9JyaJ/l3d+vfyqL6nnmP0rFbNIITcnaWGLoFhecHISsWkr8c960fTnEl35WF2lVRCXrxeMnleTJsWdjYSwt/LpleYS4cQR57mGIandaxIKs4uuBd67ukqJcUErb7XEzAMx7Kosa+8aWPlW68H3D3K8zRL05zFzZt0OpYkULsg+9AQB2TwcXI8onmW4bRqBioV8Miap44TSYngHu5t+noKMotlOEbP6TUsxsjW5Z6blmM4c5cfxoihOTGJm3V0CerqVn0HmqP1XF0BlpihOZLCgZ1dg3u4CxcyepahedMwVIxsnSWGzRw4s7kmWssSBG7czqlZR5fqEG0eMQzH0pxFrznP8XoNK1WKgrq6te7pbkg1wbI8J7RrLfeCWWBWxTjbi1/t7flqfy8zC2xaRDxCTvZiQ7B7jRZYx0nF5OAe7oNf8kSC75zmGD0rmHQPF6lBk9N3VwDJpKRh7pHnEX3/2l+MkYeLBQt8MrLws19utm9pJxERjX3kvTu6tG3lcM8CS0RZxhbYRYpEBG+kdhiaYxnU3F/RoqnthDGNTCywRfmt1bIkgdq3cujQ1vHZscAg3GsU0AzNWdzlmGN5KxdYsDTP1n/BAo8QS3NsXRss8xxiOa72V2AZjmWQ9WtJeR4xeo7RI4QgZBZ4XNAMRzMc0lrb01jT3FiOZ7WsxUTLtKV0EBzP62m+9oe0oC8Qfzu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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "\n",
    "Image(filename='df.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 创建DataFrame\n",
    "* 通过dict of lists\n",
    "* 通过list of dicts\n",
    "* 通过dict of dict/series\n",
    "* 通过array\n",
    "* 通过文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dict of lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'apples':[3,2,0,1],\n",
    "'oranges':[0,3,7,2]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### List of dicts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([{'apples':3,'oranges':0},\n",
    "{'apples':2,'oranges':3},\n",
    "{'apples':0,'oranges':7},\n",
    "{'apples':1,'oranges':2}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dict of series/dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'apples':pd.Series([3,2,0,1]),\n",
    "'oranges':pd.Series([0,3,7,2])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'apples':{0:3,1:2,2:0,3:1},\n",
    "'oranges':{0:0,1:3,2:7,3:2}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pd.Series([3,2,0,1],name='apples'),\n",
    "pd.Series([0,3,7,2],name='oranges')]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.array([[3,2,0,1],[0,3,7,2]]),index=['apples','oranges']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data\\\\df.csv',\n",
       " './data\\\\score.xlsx',\n",
       " './data\\\\stock.csv',\n",
       " './data\\\\weather-6m.csv']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "glob.glob('./data/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-133.0</td>\n",
       "      <td>-161.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.0</td>\n",
       "      <td>-156.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-111.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-94.0</td>\n",
       "      <td>-144.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-89.0</td>\n",
       "      <td>-139.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-83.0</td>\n",
       "      <td>-139.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-78.0</td>\n",
       "      <td>-139.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month  day  hour  air_temp  dew_point  wind_speed\n",
       "0 2009-01-01      1    1     1    -133.0     -167.0        15.0\n",
       "1 2009-01-01      1    1     2    -133.0     -161.0        26.0\n",
       "2 2009-01-01      1    1     3    -122.0     -156.0         0.0\n",
       "3 2009-01-01      1    1     4    -117.0     -150.0         0.0\n",
       "4 2009-01-01      1    1     5    -111.0     -150.0        15.0\n",
       "5 2009-01-01      1    1     6       NaN        NaN         NaN\n",
       "6 2009-01-01      1    1     7     -94.0     -144.0        31.0\n",
       "7 2009-01-01      1    1     8     -89.0     -139.0        31.0\n",
       "8 2009-01-01      1    1     9     -83.0     -139.0        41.0\n",
       "9 2009-01-01      1    1    10     -78.0     -139.0        57.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/weather-6m.csv',sep=',',nrows=10,header=0,parse_dates=['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlrd\n",
      "  Downloading xlrd-1.2.0-py2.py3-none-any.whl (103 kB)\n",
      "Installing collected packages: xlrd\n",
      "Successfully installed xlrd-1.2.0\n"
     ]
    }
   ],
   "source": [
    "! pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>english</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>chinese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    class\n",
       "0 2020-01-01  english\n",
       "1 2020-01-02  chinese"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('./data/score.xlsx',nrows=2,header=0,sheet_name=[''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## DataFrame选择\n",
    "* 使用column选择\n",
    "* 使用index选择\n",
    "* 使用位置选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 使用column选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'apples':pd.Series([3,2,0,1]),\n",
    "'oranges':pd.Series([0,3,7,2])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apples</th>\n",
       "      <th>oranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges\n",
       "0       3        0\n",
       "1       2        3\n",
       "2       0        7\n",
       "3       1        2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    2\n",
       "2    0\n",
       "3    1\n",
       "Name: apples, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['apples']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    2\n",
       "2    0\n",
       "3    1\n",
       "Name: apples, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用index选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apples     3\n",
       "oranges    0\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apples     3\n",
       "oranges    0\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用位置选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apples     2\n",
       "oranges    3\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课后练习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'ticker': ['AAPL', 'AAPL', 'MSFT', 'IBM', 'YHOO'],\n",
    "    'date': ['2015-12-30', '2015-12-31', '2015-12-30', '2015-12-30', '2015-12-30'],\n",
    "    'open': [426.23, 427.81, 42.3, 101.65, 35.53]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用两种方法选择open，结果是一个series\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择open，结果是一个dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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